Last Updated on November 6, 2023 by GeeksGod

Course : Deployment of Machine Learning Models in Production | Python

Deploy ML Model with BERT, DistilBERT, FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2

Welcome to “Deploy ML Model with BERT, DistilBERT, FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2”! In this course, you will learn how to deploy natural language processing (NLP) models using state-of-the-art techniques such as BERT and DistilBERT, as well as FastText, in a production environment.

You will learn how to use Flask, uWSGI, and NGINX to create a web application that serves your machine-learning models. You will also learn how to deploy your application on the AWS EC2 platform, allowing you to easily scale your application as needed.

Throughout the course, you will gain hands-on experience in setting up and configuring an end-to-end machine-learning production pipeline. You will learn how to optimize and fine-tune your NLP models for production use, and how to handle scaling and performance issues.

By the end of this course, you will have the skills and knowledge needed to deploy your own NLP models in a production environment using the latest techniques and technologies. Whether you’re a data scientist, machine learning engineer, or developer, this course will provide you with the tools and skills you need to take your machine learning projects to the next level.

So, don’t wait any longer and enroll today to learn how to deploy ML Model with BERT, DistilBERT, and FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2!

Course Overview

This course is suitable for the following individuals:

• Data scientists who want to learn how to deploy their machine learning models in a production environment.
• Machine learning engineers who want to gain hands-on experience in setting up and configuring an end-to-end machine learning production pipeline.
• Developers who are interested in using technologies such as NGINX, FLASK, uWSGI, FastText, TensorFlow, and ktrain to deploy machine learning models in production.
• Individuals who want to learn how to optimize and fine-tune machine learning models for production use.
• Professionals who want to learn how to handle scaling and performance issues when deploying machine learning models in production.
• Anyone who wants to make a career in machine learning and wants to learn about production deployment.
• Anyone who wants to learn about the end-to-end pipeline of machine learning models from training to deployment.
• Anyone who wants to learn about the best practices and techniques for deploying machine learning models in a production environment.

What you will learn in this course

In this course, you will:

• Learn how to deploy machine learning models using NGINX as a web server, FLASK as a web framework, and uWSGI as a bridge between the two.
• Learn how to use FastText for natural language processing tasks in production and integrate it with TensorFlow for more advanced machine learning models.
• Learn how to use ktrain, a library built on top of TensorFlow, to easily train and deploy models in a production environment.
• Gain hands-on experience in setting up and configuring an end-to-end machine-learning production pipeline using the aforementioned technologies.
• Learn how to optimize and fine-tune machine learning models for production use, and how to handle scaling and performance issues.

All these things will be done on Google Colab, which means it doesn’t matter what processor and computer you have. It is super easy to use, and the plus point is that you have a Free GPU to use in your notebook.

Why Deploy ML Models in Production?

Deploying machine learning models in production allows you to make use of your models to benefit customers, users, and stakeholders. It enables your models to have a real-world impact and generate value. By deploying machine learning models, you can:

• Provide predictions and recommendations to users in real-time.
• Automate decision-making processes.
• Improve business operations and efficiency.
• Personalize user experiences.
• Enhance product and service offerings.

Benefits of Deploying ML Models with BERT, DistilBERT, and FastText

The use of advanced natural language processing techniques such as BERT, DistilBERT, and FastText provides several benefits when deploying machine learning models:

• Improved accuracy and performance in natural language processing tasks.
• Ability to handle complex language patterns and nuances.
• Efficient processing and understanding of textual data.
• Effective representation of words and sentences.

Building a Web Application with Flask, uWSGI, and NGINX

In this course, you will learn how to build a web application for serving your machine learning models using Flask as the web framework, uWSGI as the interface server, and NGINX as the web server. This combination of technologies provides a robust and efficient setup for deploying machine learning models in a production environment. With Flask, uWSGI, and NGINX, you can handle multiple requests, scale your application, and ensure high availability.

Deploying on AWS EC2 for Scalability

AWS EC2 is a scalable and flexible platform that allows you to deploy your web application and machine learning models. By deploying on AWS EC2, you can easily scale your application to handle increased traffic and demand. Additionally, AWS EC2 provides secure infrastructure and a range of features that ensure your application performs optimally.

Optimizing and Fine-tuning NLP Models for Production

In this course, you will learn how to optimize and fine-tune your NLP models for production use. Optimization involves improving the efficiency, accuracy, and performance of your models. Fine-tuning refers to adjusting the parameters and configurations of your models to achieve better results. By optimizing and fine-tuning your NLP models, you can ensure they perform optimally when deployed in a production environment.

Handling Scaling and Performance Issues

When deploying machine learning models in a production environment, it is vital to consider scaling and performance issues. As the number of users and requests increases, your application should be able to handle the additional load efficiently. It is crucial to optimize your infrastructure, code, and model to ensure smooth operation and minimal latency. In this course, you will learn best practices for handling scaling and performance issues in a production environment.

Conclusion

Deploying ML Model with BERT, DistilBERT, and FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2 is a comprehensive course that equips you with the knowledge and skills to deploy machine learning models using state-of-the-art techniques. By following this course, you will be able to take your machine learning projects to the next level and make a real-world impact with your models.